摘要
This research presents an innovative approach to accurately predict the nugget diameter in resistance spot welding(RSW)by leveraging machine learning and transfer learning methods.Initially,low-fidelity(LF)data were obtained through finite element numerical simulation and design of experiments(DOEs)to train the LF machine learning model.Subsequently,high-fidelity(HF)data were collected from RSW process experiments and used to fine-tune the LF model by transfer learning techniques.The accuracy and generalization performance of the models were thoroughly validated.The results demonstrated that combining different fidelity datasets and employing transfer learning could significantly improve the prediction accuracy while minimize the costs associated with experimental trials,and provide an effective and valuable method for predicting critical process parameters in RSW.
基金
founded by the Construction Project of the National Natural Science Foundation(Grant No.52205377)
the National Key Research and Development Program(Grant No.2022YFB4601804)
the Key Basic Research Project of Suzhou(Grant Nos.SJC2022029,SJC2022031).
